Advanced cybersecurity threat mitigation using software supply chain analysis

ABSTRACT

A system and method for comprehensive cybersecurity threat assessment of software applications based on the totality of vulnerabilities from all levels of the software supply chain. The system and method comprising analyzing the code and/or operation of a software application to determine components comprising the software, identifying the source of such components, determining vulnerabilities associated with those components, compiling a list of such components, creating a directed graph of relationships between the components and their sources, and evaluating the overall threat associated with the software application based its software supply chain vulnerabilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

Application No. Date Filed Title Current Herewith HOLISTIC COMPUTERSYSTEM application CYBERSECURITY EVALUATION AND SCORINGIs a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTERSYSTEM CYBERSECURITY EVALUATION AND SCORINGwhich is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM ANDMETHODS FOR SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT,DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/818,733Nov. 20, 2017 SYSTEM AND METHOD FOR CYBERSECURITY ANALYSIS AND SCOREGENERATION FOR INSURANCE PURPOSES which is a continuation-in-part of:15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED Patent Issue DateCYBERSECURITY THREAT 10,609,079 Mar. 31, 2020 MITIGATION TO ROGUEDEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCHMANAGEMENT which is a continuation-in-part of: 15/655,113 Jul. 20, 2017ADVANCED CYBERSECURITY THREAT MITIGATION USING BEHAVIORAL AND DEEPANALYTICS which is a continuation-in-part of: 15/616,427 Jun. 7, 2017RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVENDISTRIBUTED COMPUTATIONAL GRAPH and is also a continuation-in-part of:15/237,625 Aug. 15, 2016 DETECTION MITIGATION Patent Issue Date ANDREMEDIATION OF 10,248,910 Apr. 2, 2019 CYBERATTACKS EMPLOYING ANADVANCED CYBER- DECISION PLATFORM which is a continuation-in-part of:15/206,195 Jul. 8, 2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITHLARGE COMPLEX DATASETS USING A DISTRIBUTED SIMULATION ENGINEwhich is a continuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FORAUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLEBUSINESS VENTURE OUTCOME PREDICTION which is a continuation-in-part of:15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OFBUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURERELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESSINFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATIONwhich is a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FORCAPTURE, Patented Issued Date ANALYSIS AND STORAGE OF 10,204,147 Feb.12, 2019 TIME SERIES DATA FROM SENSORS WITH HETEROGENEOUS REPORTINTERVAL PROFILES and is also a continuation-in-part of: 14/986,536 Dec.31, 2015 DISTRIBUTED SYSTEM FOR Patented Issued Date LARGE VOLUME DEEPWEB 10,210,255 Feb. 19, 2019 DATA EXTRACTIONand is also a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPIDPREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTEDCOMPUTATIONAL GRAPH Current Herewith ADVANCED CYBERSECURITY applicationTHREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSISIs a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTERSYSTEM CYBERSECURITY EVALUATION AND SCORINGwhich is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM ANDMETHODS FOR SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT,DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/823,285Nov. 27, 2017 META-INDEXING, SEARCH, COMPLIANCE, AND TEST FRAMEWORK FORSOFTWARE DEVELOPMENT which is a continuation-in-part of: 15/788,718 Oct.19, 2017 DATA MONETIZATION AND EXCHANGE PLATFORMwhich claims priority, and benefit to: 62/568,307 Oct. 4, 2017 DATAMONETIZATION AND EXCHANGE PLATFORMand is also a continuation-in-part of: 15/788,002 Oct. 19, 2017ALGORITHM MONETIZATION AND EXCHANGE PLATFORMwhich claims priority, and benefit to: 62/568,305 Oct. 4, 2017 ALGORITHMMONETIZATION AND EXCHANGE PLATFORMand is also a continuation-in-part of: 15/787,601 Oct. 18, 2017 METHODAND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, ANDCOMPENSATION which claims priority, and benefit to: 62/568,312 Oct. 4,2017 METHOD AND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION,AND COMPENSATION and is also a continuation-in-part of: 15/616,427 Jun.7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING ANACTOR-DRIVEN DISTRIBUTED COMPUTATIONAL GRAPHwhich is a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPIDPREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTEDCOMPUTATIONAL GRAPHY Current Herewith ADVANCED CYBERSECURITY applicationTHREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSISIs a continuation-in-part of: 16/777,270 Jan. 30, 2020 CYBERSECURITYPROFILING AND RATING USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCEwhich is a continuation-in-part of: 16/720,383 Dec. 19, 2019 RATINGORGANIZATION CYBERSECURITY USING ACTIVE AND PASSIVE EXTERNALRECONNAISSANCE which is a continuation of: 15/823,363 Nov. 27, 2017RATING ORGANIZATION CYBERSECURITY USING ACTIVE AND PASSIVE EXTERNALRECONNAISSANCE which is a continuation-in-part of: 15/725,274 Oct. 4,2017 APPLICATION OF ADVANCED CYBERSECURITY THREAT MITIGATION TO ROGUEDEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCHMANAGEMENT the entire specification of each of which is incorporatedherein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The disclosure relates to the field of computer management, and moreparticularly to the field of cybersecurity and threat analytics.

Discussion of the State of the Art

Analysis of the cybersecurity of software has tended to focus on thesoftware application itself, but has largely ignored cybersecurity risksassociated with other links in the software development supply chain.Modern software development relies on incorporation of many componentsupstream of the development of the software application and is subjectto vulnerabilities associated with dependencies or data from downstreamof the application. The overall cybersecurity risks of a softwareapplication, therefore, depends on a complex chain of vulnerabilitiesthat may be introduced at many stages of the software development supplychain. There is currently no system or method for assessing thecomplexity of these incorporations and dependencies and their impact onthe cybersecurity of a software application and its use.

What is needed is a system and method for identifying, tracing, andanalyzing each component or service that is incorporated into,contributes to, or is used by a software application from all stages ofthe software supply chain, such that an comprehensive assessment of allcybersecurity threats associated with a software application can bemade.

SUMMARY OF THE INVENTION

Accordingly, the inventor has developed, and reduced to practice, asystem and method for comprehensive cybersecurity threat assessment ofsoftware applications based on the totality of vulnerabilities from alllevels of the software supply chain. The system and method comprisinganalyzing the code and/or operation of a software application todetermine components comprising the software, identifying the source ofsuch components, determining vulnerabilities associated with thosecomponents, compiling a list of such components, creating a directedgraph of relationships between the components and their sources, andevaluating the overall threat associated with the software applicationbased its software supply chain vulnerabilities.

According to a preferred embodiment, a system for analyzing thecybersecurity threat of software applications from the software supplychain is disclosed, comprising: a computing device comprising a memoryand a processor; a software analyzer comprising a first plurality ofprogramming instructions stored in the memory of, and operating on theprocessor of, the computing device, wherein the first plurality ofprogramming instructions, when operating on the processor, cause thecomputing device to: receive a software application for analysis;identify one or more software components comprising the softwareapplication; and send a component identifier for each software componentidentified to a reconnaissance engine; a reconnaissance enginecomprising a second plurality of programming instructions stored in thememory of, and operating on the processor of, the computing device,wherein the second plurality of programming instructions, when operatingon the processor, cause the computing device to: receive the componentidentifier for the one or more software components; search one or moredatabases to identify a source of each software component; search one ormore databases to identify a vulnerability of each software component;send the component identifier, source, and vulnerability for each of theone or more software components to a cyber-physical graph engine; acyber-physical graph engine comprising a third plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the third plurality of programminginstructions, when operating on the processor, cause the computingdevice to: receive the component identifier, source, and vulnerabilityfor each of the one or more software components; and construct acyber-physical graph of a software supply chain for the softwareapplication, the cyber-physical graph comprising nodes representing thesource and vulnerability of each software component of the softwareapplication and edges representing the relationships between the nodes;and a scoring engine comprising a third plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the third plurality of programminginstructions, when operating on the processor, cause the computingdevice to: run one or more graph-processing algorithms on thecyber-physical graph to determine one or more paths of vulnerability inthe software supply chain and a probability of occurrence for each path;and generate a cybersecurity score for the software application based onthe vulnerabilities in the software supply chain.

According to another preferred embodiment, a method for analyzing thecybersecurity threat of software applications from the software supplychain is disclosed, comprising the steps of: receiving a softwareapplication for analysis; identifying one or more software componentscomprising the software application; searching one or more databases toidentify a source of each software component; searching one or moredatabases to identify a vulnerability of each software component;constructing a cyber-physical graph of a software supply chain for thesoftware application, the cyber-physical graph comprising nodesrepresenting the source and vulnerability of each software component ofthe software application and edges representing the relationshipsbetween the nodes; running one or more graph-processing algorithms onthe cyber-physical graph to determine one or more paths of vulnerabilityin the software supply chain and a probability of occurrence for eachpath; and generating a cybersecurity score for the software applicationbased on the vulnerabilities in the software supply chain.

According to an aspect of an embodiment, one of the databases used toidentify a dependency of each software component is a softwaredependency database containing structured information.

According to an aspect of an embodiment, one of the databases used toidentify a vulnerability of each software component is a vulnerabilityinformation database containing structured information.

According to an aspect of an embodiment, a natural language processingengine is used to: retrieve structured data from a source ofvulnerability information; retrieve unstructured data from a differentsource of vulnerability information; extract identifiable informationregarding vulnerabilities from the structured data; search, identify,and tag the unstructured data using the identifiable information fromthe structured data, thereby converting the unstructured data to newlystructured data; and storing a database comprising the newly structureddata; wherein one of the databases used to identify a vulnerability ofeach software component is the database comprising the newly structureddata, or one of the databases used to identify a source of each softwarecomponent is the database comprising the newly structured data, or both.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram of an exemplary system architecture for anadvanced cyber decision platform.

FIG. 2 is a block diagram of an advanced cyber decision platform in anexemplary configuration for use in investment vehicle management.

FIG. 3 is a process diagram showing advanced cyber decision platformfunctions in use to mitigate cyberattacks.

FIG. 4 is a process flow diagram of a method for segmenting cyberattackinformation to appropriate corporation parties.

FIG. 5 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 6 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 7 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph, according to one aspect.

FIG. 8 is a flow diagram of an exemplary method for cybersecuritybehavioral analytics, according to one aspect.

FIG. 9 is a flow diagram of an exemplary method for measuring theeffects of cybersecurity attacks, according to one aspect.

FIG. 10 is a flow diagram of an exemplary method for continuouscybersecurity monitoring and exploration, according to one aspect.

FIG. 11 is a flow diagram of an exemplary method for mapping acyber-physical system graph, according to one aspect.

FIG. 12 is a flow diagram of an exemplary method for continuous networkresilience scoring, according to one aspect.

FIG. 13 is a flow diagram of an exemplary method for cybersecurityprivilege oversight, according to one aspect.

FIG. 14 is a flow diagram of an exemplary method for cybersecurity riskmanagement, according to one aspect.

FIG. 15 is a flow diagram of an exemplary method for mitigatingcompromised credential threats, according to one aspect.

FIG. 16 is a flow diagram of an exemplary method for dynamic network androgue device discovery, according to one aspect.

FIG. 17 is a flow diagram of an exemplary method for Kerberos “goldenticket” attack detection, according to one aspect.

FIG. 18 is a flow diagram of an exemplary method for risk-basedvulnerability and patch management, according to one aspect.

FIG. 19 is block diagram showing an exemplary system architecture for asystem for cybersecurity profiling and rating.

FIG. 20 is a relational diagram showing the relationships betweenexemplary 3rd party search tools, search tasks that can be generatedusing such tools, and the types of information that may be gathered withthose tasks.

FIG. 21 is a block diagram showing an exemplary architecture for asoftware analyzer for a holistic computer system cybersecurityevaluation and scoring system.

FIG. 22 is a block diagram showing exemplary elements of a softwaresupply chain with upstream and downstream sources of cybersecurityvulnerabilities.

FIG. 23 is an exemplary cyber-physical graph showing a software supplychain represented as a directed graph with identification of the sourcesof specific software components and possible vulnerabilities.

FIG. 24 is a block diagram showing an overall system architecture for asupply chain vulnerability analysis system.

FIG. 25 is a block diagram showing an exemplary architecture for anatural language processing engine for the extraction and processing ofdata using natural language processing from labeled, unlabeled, andpartially-labeled sources of software vulnerability information.

FIG. 26 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 27 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 28 is a block diagram illustrating an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 29 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor comprehensive cybersecurity threat assessment of softwareapplications based on the totality of vulnerabilities from all levels ofthe software supply chain. The system and method comprising analyzingthe code and/or operation of a software application to determinecomponents comprising the software, identifying the source of suchcomponents, determining vulnerabilities associated with thosecomponents, compiling a list of such components, creating a directedgraph of relationships between the components and their sources, andevaluating the overall threat associated with the software applicationbased its software supply chain vulnerabilities. The system and methodmay further contain a natural language processing engine which receivesstructured and unstructured data from one or more sources ofvulnerability information, and uses entity recognition and labelinginformation contained in the structured data to search, identify, andtag information in the unstructured information, so that it can bereorganized into structured information and used as a database inanalyzing software supply chain vulnerabilities.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

As used herein, “graph” is a representation of information andrelationships, where each primary unit of information makes up a “node”or “vertex” of the graph and the relationship between two nodes makes upan edge of the graph. Nodes can be further qualified by the connectionof one or more descriptors or “properties” to that node. For example,given the node “James R,” name information for a person, qualifyingproperties might be “183 cm tall”, “DOB Aug. 13, 1965” and “speaksEnglish”. Similar to the use of properties to further describe theinformation in a node, a relationship between two nodes that forms anedge can be qualified using a “label”. Thus, given a second node “ThomasG,” an edge between “James R” and “Thomas G” that indicates that the twopeople know each other might be labeled “knows.” When graph theorynotation (Graph=(Vertices, Edges)) is applied this situation, the set ofnodes are used as one parameter of the ordered pair, V and the set of 2element edge endpoints are used as the second parameter of the orderedpair, E. When the order of the edge endpoints within the pairs of E isnot significant, for example, the edge James R, Thomas G is equivalentto Thomas G, James R, the graph is designated as “undirected.” Undercircumstances when a relationship flows from one node to another in onedirection, for example James R is “taller” than Thomas G, the order ofthe endpoints is significant. Graphs with such edges are designated as“directed.” In the distributed computational graph system,transformations within transformation pipeline are represented asdirected graph with each transformation comprising a node and the outputmessages between transformations comprising edges. Distributedcomputational graph stipulates the potential use of non-lineartransformation pipelines which are programmatically linearized. Suchlinearization can result in exponential growth of resource consumption.The most sensible approach to overcome possibility is to introduce newtransformation pipelines just as they are needed, creating only thosethat are ready to compute. Such method results in transformation graphswhich are highly variable in size and node, edge composition as thesystem processes data streams. Those familiar with the art will realizethat transformation graph may assume many shapes and sizes with a vasttopography of edge relationships. The examples given were chosen forillustrative purposes only and represent a small number of the simplestof possibilities. These examples should not be taken to define thepossible graphs expected as part of operation of the invention

As used herein, “transformation” is a function performed on zero or morestreams of input data which results in a single stream of output whichmay or may not then be used as input for another transformation.Transformations may comprise any combination of machine, human ormachine-human interactions Transformations need not change data thatenters them, one example of this type of transformation would be astorage transformation which would receive input and then act as a queuefor that data for subsequent transformations. As implied above, aspecific transformation may generate output data in the absence of inputdata. A time stamp serves as a example. In the invention,transformations are placed into pipelines such that the output of onetransformation may serve as an input for another. These pipelines canconsist of two or more transformations with the number oftransformations limited only by the resources of the system.Historically, transformation pipelines have been linear with eachtransformation in the pipeline receiving input from one antecedent andproviding output to one subsequent with no branching or iteration. Otherpipeline configurations are possible. The invention is designed topermit several of these configurations including, but not limited to:linear, afferent branch, efferent branch and cyclical.

A “database” or “data storage subsystem” (these terms may be consideredsubstantially synonymous), as used herein, is a system adapted for thelong-term storage, indexing, and retrieval of data, the retrievaltypically being via some sort of querying interface or language.“Database” may be used to refer to relational database managementsystems known in the art, but should not be considered to be limited tosuch systems. Many alternative database or data storage systemtechnologies have been, and indeed are being, introduced in the art,including but not limited to distributed non-relational data storagesystems such as Hadoop, column-oriented databases, in-memory databases,and the like. While various aspects may preferentially employ one oranother of the various data storage subsystems available in the art (oravailable in the future), the invention should not be construed to be solimited, as any data storage architecture may be used according to theaspects. Similarly, while in some cases one or more particular datastorage needs are described as being satisfied by separate components(for example, an expanded private capital markets database and aconfiguration database), these descriptions refer to functional uses ofdata storage systems and do not refer to their physical architecture.For instance, any group of data storage systems of databases referred toherein may be included together in a single database management systemoperating on a single machine, or they may be included in a singledatabase management system operating on a cluster of machines as isknown in the art. Similarly, any single database (such as an expandedprivate capital markets database) may be implemented on a singlemachine, on a set of machines using clustering technology, on severalmachines connected by one or more messaging systems known in the art, orin a master/slave arrangement common in the art. These examples shouldmake clear that no particular architectural approaches to databasemanagement is preferred according to the invention, and choice of datastorage technology is at the discretion of each implementer, withoutdeparting from the scope of the invention as claimed.

A “data context”, as used herein, refers to a set of argumentsidentifying the location of data. This could be a Rabbit queue, a .csvfile in cloud-based storage, or any other such location reference excepta single event or record. Activities may pass either events or datacontexts to each other for processing. The nature of a pipeline allowsfor direct information passing between activities, and data locations orfiles do not need to be predetermined at pipeline start.

A “pipeline”, as used herein and interchangeably referred to as a “datapipeline” or a “processing pipeline”, refers to a set of data streamingactivities and batch activities. Streaming and batch activities can beconnected indiscriminately within a pipeline. Events will flow throughthe streaming activity actors in a reactive way. At the junction of astreaming activity to batch activity, there will exist aStreamBatchProtocol data object. This object is responsible fordetermining when and if the batch process is run. One or more of threepossibilities can be used for processing triggers: regular timinginterval, every N events, or optionally an external trigger. The eventsare held in a queue or similar until processing. Each batch activity maycontain a “source” data context (this may be a streaming context if theupstream activities are streaming), and a “destination” data context(which is passed to the next activity). Streaming activities may have anoptional “destination” streaming data context (optional meaning:caching/persistence of events vs. ephemeral), though this should not bepart of the initial implementation.

Conceptual Architecture

FIG. 1 is a block diagram of an advanced cyber decision platform. Clientaccess to the system 105 for specific data entry, system control and forinteraction with system output such as automated predictive decisionmaking and planning and alternate pathway simulations, occurs throughthe system's distributed, extensible high bandwidth cloud interface 110which uses a versatile, robust web application driven interface for bothinput and display of client-facing information via network 107 andoperates a data store 112 such as, but not limited to MONGODB™,COUCHDB™, CASSANDRA™ or REDIS™ according to various arrangements. Muchof the business data analyzed by the system both from sources within theconfines of the client business, and from cloud based sources, alsoenter the system through the cloud interface 110, data being passed tothe connector module 135 which may possess the API routines 135 a neededto accept and convert the external data and then pass the normalizedinformation to other analysis and transformation components of thesystem, the directed computational graph module 155, high volume webcrawler module 115, multidimensional time series database (MDTSDB) 120and the graph stack service 145. The directed computational graph module155 retrieves one or more streams of data from a plurality of sources,which includes, but is in no way not limited to, a plurality of physicalsensors, network service providers, web based questionnaires andsurveys, monitoring of electronic infrastructure, crowd sourcingcampaigns, and human input device information. Within the directedcomputational graph module 155, data may be split into two identicalstreams in a specialized pre-programmed data pipeline 155 a, wherein onesub-stream may be sent for batch processing and storage while the othersub-stream may be reformatted for transformation pipeline analysis. Thedata is then transferred to the general transformer service module 160for linear data transformation as part of analysis or the decomposabletransformer service module 150 for branching or iterativetransformations that are part of analysis. The directed computationalgraph module 155 represents all data as directed graphs where thetransformations are nodes and the result messages betweentransformations edges of the graph. The high volume web crawling module115 uses multiple server hosted preprogrammed web spiders, which whileautonomously configured are deployed within a web scraping framework 115a of which SCRAPY™ is an example, to identify and retrieve data ofinterest from web based sources that are not well tagged by conventionalweb crawling technology. The multiple dimension time series data storemodule 120 may receive streaming data from a large plurality of sensorsthat may be of several different types. The multiple dimension timeseries data store module may also store any time series data encounteredby the system such as but not limited to enterprise network usage data,component and system logs, performance data, network service informationcaptures such as, but not limited to news and financial feeds, and salesand service related customer data. The module is designed to accommodateirregular and high volume surges by dynamically allotting networkbandwidth and server processing channels to process the incoming data.Inclusion of programming wrappers 120 a for languages examples of whichare, but not limited to C++, PERL, PYTHON, and ERLANG™ allowssophisticated programming logic to be added to the default function ofthe multidimensional time series database 120 without intimate knowledgeof the core programming, greatly extending breadth of function. Dataretrieved by the multidimensional time series database (MDTSDB) 120 andthe high volume web crawling module 115 may be further analyzed andtransformed into task optimized results by the directed computationalgraph 155 and associated general transformer service 150 anddecomposable transformer service 160 modules. Alternately, data from themultidimensional time series database and high volume web crawlingmodules may be sent, often with scripted cuing information determiningimportant vertexes 145 a, to the graph stack service module 145 which,employing standardized protocols for converting streams of informationinto graph representations of that data, for example, open graphinternet technology although the invention is not reliant on any onestandard. Through the steps, the graph stack service module 145represents data in graphical form influenced by any pre-determinedscripted modifications 145 a and stores it in a graph-based data store145 b such as GIRAPH™ or a key value pair type data store REDIS™, orRIAK™, among others, all of which are suitable for storing graph-basedinformation.

Results of the transformative analysis process may then be combined withfurther client directives, additional business rules and practicesrelevant to the analysis and situational information external to thealready available data in the automated planning service module 130which also runs powerful information theory 130 a based predictivestatistics functions and machine learning algorithms to allow futuretrends and outcomes to be rapidly forecast based upon the current systemderived results and choosing each a plurality of possible businessdecisions. The using all available data, the automated planning servicemodule 130 may propose business decisions most likely to result is themost favorable business outcome with a usably high level of certainty.Closely related to the automated planning service module in the use ofsystem derived results in conjunction with possible externally suppliedadditional information in the assistance of end user business decisionmaking, the action outcome simulation module 125 with its discrete eventsimulator programming module 125 a coupled with the end user facingobservation and state estimation service 140 which is highly scriptable140 b as circumstances require and has a game engine 140 a to morerealistically stage possible outcomes of business decisions underconsideration, allows business decision makers to investigate theprobable outcomes of choosing one pending course of action over anotherbased upon analysis of the current available data.

When performing external reconnaissance via a network 107, web crawler115 may be used to perform a variety of port and service scanningoperations on a plurality of hosts. This may be used to targetindividual network hosts (for example, to examine a specific server orclient device) or to broadly scan any number of hosts (such as all hostswithin a particular domain, or any number of hosts up to the completeIPv4 address space). Port scanning is primarily used for gatheringinformation about hosts and services connected to a network, using probemessages sent to hosts that prompt a response from that host. Portscanning is generally centered around the transmission control protocol(TCP), and using the information provided in a prompted response a portscan can provide information about network and application layers on thetargeted host.

Port scan results can yield information on open, closed, or undeterminedports on a target host. An open port indicated that an application orservice is accepting connections on this port (such as ports used forreceiving customer web traffic on a web server), and these portsgenerally disclose the greatest quantity of useful information about thehost. A closed port indicates that no application or service islistening for connections on that port, and still provides informationabout the host such as revealing the operating system of the host, whichmay discovered by fingerprinting the TCP/IP stack in a response.Different operating systems exhibit identifiable behaviors whenpopulating TCP fields, and collecting multiple responses and matchingthe fields against a database of known fingerprints makes it possible todetermine the OS of the host even when no ports are open. Anundetermined port is one that does not produce a requested response,generally because the port is being filtered by a firewall on the hostor between the host and the network (for example, a corporate firewallbehind which all internal servers operate).

Scanning may be defined by scope to limit the scan according to twodimensions, hosts and ports. A horizontal scan checks the same port onmultiple hosts, often used by attackers to check for an open port on anyavailable hosts to select a target for an attack that exploits avulnerability using that port. This type of scan is also useful forsecurity audits, to ensure that vulnerabilities are not exposed on anyof the target hosts. A vertical scan defines multiple ports to examineon a single host, for example a “vanilla scan” which targets every portof a single host, or a “strobe scan” that targets a small subset ofports on the host. This type of scan is usually performed forvulnerability detection on single systems, and due to the single-hostnature is impractical for large network scans. A block scan combineselements of both horizontal and vertical scanning, to scan multipleports on multiple hosts. This type of scan is useful for a variety ofservice discovery and data collection tasks, as it allows a broad scanof many hosts (up to the entire Internet, using the complete IPv4address space) for a number of desired ports in a single sweep.

Large port scans involve quantitative research, and as such may betreated as experimental scientific measurement and are subject tomeasurement and quality standards to ensure the usefulness of results.To avoid observational errors during measurement, results must beprecise (describing a degree of relative proximity between individualmeasured values), accurate (describing relative proximity of measuredvalues to a reference value), preserve any metadata that accompanies themeasured data, avoid misinterpretation of data due to faulty measurementexecution, and must be well-calibrated to efficiently expose and addressissues of inaccuracy or misinterpretation. In addition to these basicrequirements, large volumes of data may lead to unexpected behavior ofanalysis tools, and extracting a subset to perform initial analysis mayhelp to provide an initial overview before working with the completedata set. Analysis should also be reproducible, as with all experimentalscience, and should incorporate publicly-available data to add value tothe comprehensibility of the research as well as contributing to a“common framework” that may be used to confirm results.

When performing a port scan, web crawler 115 may employ a variety ofsoftware suitable for the task, such as Nmap, ZMap, or masscan. Nmap issuitable for large scans as well as scanning individual hosts, andexcels in offering a variety of diverse scanning techniques. ZMap is anewer application and unlike Nmap (which is more general-purpose), ZMapis designed specifically with Internet-wide scans as the intent. As aresult, ZMap is far less customizable and relies on horizontal portscans for functionality, achieving fast scan times using techniques ofprobe randomization (randomizing the order in which probes are sent tohosts, minimizing network saturation) and asynchronous design (utilizingstateless operation to send and receive packets in separate processingthreads). Masscan uses the same asynchronous operation model of ZMap, aswell as probe randomization. In masscan however, a certain degree ofstatistical randomness is sacrificed to improve computation time forlarge scans (such as when scanning the entire IPv4 address space), usingthe BlackRock algorithm. This is a modified implementation of symmetricencryption algorithm DES, with fewer rounds and modulo operations inplace of binary ones to allow for arbitrary ranges and achieve fastercomputation time for large data sets.

Received scan responses may be collected and processed through aplurality of data pipelines 155 a to analyze the collected information.MDTSDB 120 and graph stack 145 may be used to produce a hybridgraph/time-series database using the analyzed data, forming a graph ofInternet-accessible organization resources and their evolving stateinformation over time. Customer-specific profiling and scanninginformation may be linked to CPG graphs (as described below in detail,referring to FIG. 11) for a particular customer, but this informationmay be further linked to the base-level graph of internet-accessibleresources and information. Depending on customer authorizations andlegal or regulatory restrictions and authorizations, techniques used mayinvolve both passive, semi-passive and active scanning andreconnaissance.

FIG. 2 is a block diagram of an advanced cyber decision platform in anexemplary configuration for use in investment vehicle management 200.The advanced cyber decision platform 100 previously disclosed inco-pending application Ser. No. 15/141,752 and applied in a role ofcybersecurity in co-pending application Ser. No. 15/237,625, whenprogrammed to operate as quantitative trading decision platform, is verywell suited to perform advanced predictive analytics and predictivesimulations 202 to produce investment predictions. Much of the tradingspecific programming functions are added to the automated planningservice module 130 of the modified advanced cyber decision platform 100to specialize it to perform trading analytics. Specialized purposelibraries may include but are not limited to financial markets functionslibraries 251, Monte-Carlo risk routines 252, numeric analysis libraries253, deep learning libraries 254, contract manipulation functions 255,money handling functions 256, Monte-Carlo search libraries 257, andquant approach securities routines 258. Pre-existing deep learningroutines including information theory statistics engine 259 may also beused. The invention may also make use of other libraries andcapabilities that are known to those skilled in the art as instrumentalin the regulated trade of items of worth. Data from a plurality ofsources used in trade analysis are retrieved, much of it from remote,cloud resident 201 servers through the system's distributed, extensiblehigh bandwidth cloud interface 110 using the system's connector module135 which is specifically designed to accept data from a number ofinformation services both public and private through interfaces to thoseservice's applications using its messaging service 135 a routines, dueto ease of programming, are augmented with interactive broker functions235, market data source plugins 236, e-commerce messaging interpreters237, business-practice aware email reader 238 and programming librariesto extract information from video data sources 239.

Other modules that make up the advanced cyber decision platform may alsoperform significant analytical transformations on trade related data.These may include the multidimensional time series data store 120 withits robust scripting features which may include a distributive friendly,fault-tolerant, real-time, continuous run prioritizing, programmingplatform such as, but not limited to Erlang/OTP 221 and a compatible butcomprehensive and proven library of math functions of which the C⁺⁺ mathlibraries are an example 222, data formalization and ability to capturetime series data including irregularly transmitted, burst data; theGraphStack service 145 which transforms data into graphicalrepresentations for relational analysis and may use packages for graphformat data storage such as Titan 245 or the like and a highly interfaceaccessible programming interface an example of which may be Akka/Spray,although other, similar, combinations may equally serve the same purposein this role 246 to facilitate optimal data handling; the directedcomputational graph module 155 and its distributed data pipeline 155 asupplying related general transformer service module 160 anddecomposable transformer module 150 which may efficiently carry outlinear, branched, and recursive transformation pipelines during tradingdata analysis may be programmed with multiple trade related functionsinvolved in predictive analytics of the received trade data. Bothpossibly during and following predictive analyses carried out by thesystem, results must be presented to clients 105 in formats best suitedto convey the both important results for analysts to make highlyinformed decisions and, when needed, interim or final data in summaryand potentially raw for direct human analysis. Simulations which may usedata from a plurality of field spanning sources to predict future tradeconditions these are accomplished within the action outcome simulationmodule 125. Data and simulation formatting may be completed or performedby the observation and state estimation service 140 using its ease ofscripting and gaming engine to produce optimal presentation results.

In cases where there are both large amounts of data to be cleansed andformalized and then intricate transformations such as those that may beassociated with deep machine learning, first disclosed in 1067 ofco-pending application Ser. No. 14/925,974, predictive analytics andpredictive simulations, distribution of computer resources to aplurality of systems may be routinely required to accomplish these tasksdue to the volume of data being handled and acted upon. The advancedcyber decision platform employs a distributed architecture that ishighly extensible to meet these needs. A number of the tasks carried outby the system are extremely processor intensive and for these, thehighly integrated process of hardware clustering of systems, possibly ofa specific hardware architecture particularly suited to the calculationsinherent in the task, is desirable, if not required for timelycompletion. The system includes a computational clustering module 280 toallow the configuration and management of such clusters duringapplication of the advanced cyber decision platform. While thecomputational clustering module is drawn directly connected to specificco-modules of the advanced cyber decision platform these connections,while logical, are for ease of illustration and those skilled in the artwill realize that the functions attributed to specific modules of anembodiment may require clustered computing under one use case and notunder others. Similarly, the functions designated to a clusteredconfiguration may be role, if not run, dictated. Further, not all usecases or data runs may use clustering.

FIG. 3 is a process diagram showing a general flow 300 of advanced cyberdecision platform functions in use to mitigate cyberattacks. Inputnetwork data which may include network flow patterns 321, the origin anddestination of each piece of measurable network traffic 322, system logsfrom servers and workstations on the network 323, endpoint data 323 a,any security event log data from servers or available securityinformation and event (SIEM) systems 324, external threat intelligencefeeds 324 a, identity or assessment context 325, external network healthor cybersecurity feeds 326, Kerberos domain controller or ACTIVEDIRECTORY™ server logs or instrumentation 327 and business unitperformance related data 328, among many other possible data types forwhich the invention was designed to analyze and integrate, may pass into315 the advanced cyber decision platform 310 for analysis as part of itscyber security function. These multiple types of data from a pluralityof sources may be transformed for analysis 311, 312 using at least oneof the specialized cybersecurity, risk assessment or common functions ofthe advanced cyber decision platform in the role of cybersecuritysystem, such as, but not limited to network and system user privilegeoversight 331, network and system user behavior analytics 332, attackerand defender action timeline 333, SIEM integration and analysis 334,dynamic benchmarking 335, and incident identification and resolutionperformance analytics 336 among other possible cybersecurity functions;value at risk (VAR) modeling and simulation 341, anticipatory vs.reactive cost estimations of different types of data breaches toestablish priorities 342, work factor analysis 343 and cyber eventdiscovery rate 344 as part of the system's risk analytics capabilities;and the ability to format and deliver customized reports and dashboards351, perform generalized, ad hoc data analytics on demand 352,continuously monitor, process and explore incoming data for subtlechanges or diffuse informational threads 353 and generate cyber-physicalsystems graphing 354 as part of the advanced cyber decision platform'scommon capabilities. Output 317 can be used to configure network gatewaysecurity appliances 361, to assist in preventing network intrusionthrough predictive change to infrastructure recommendations 362, toalert an enterprise of ongoing cyberattack early in the attack cycle,possibly thwarting it but at least mitigating the damage 362, to recordcompliance to standardized guidelines or SLA requirements 363, tocontinuously probe existing network infrastructure and issue alerts toany changes which may make a breach more likely 364, suggest solutionsto any domain controller ticketing weaknesses detected 365, detectpresence of malware 366, and perform one time or continuousvulnerability scanning depending on client directives 367. Theseexamples are, of course, only a subset of the possible uses of thesystem, they are exemplary in nature and do not reflect any boundariesin the capabilities of the invention.

FIG. 4 is a process flow diagram of a method for segmenting cyberattackinformation to appropriate corporation parties 400. As previouslydisclosed 200, 351, one of the strengths of the advanced cyber-decisionplatform is the ability to finely customize reports and dashboards tospecific audiences, concurrently is appropriate. This customization ispossible due to the devotion of a portion of the advanced cyber decisionplatform's programming specifically to outcome presentation by moduleswhich include the observation and state estimation service 140 with itsgame engine 140 a and script interpreter 140 b. In the setting ofcybersecurity, issuance of specialized alerts, updates and reports maysignificantly assist in getting the correct mitigating actions done inthe most timely fashion while keeping all participants informed atpredesignated, appropriate granularity. Upon the detection of acyberattack by the system 401 all available information about theongoing attack and existing cybersecurity knowledge are analyzed,including through predictive simulation in near real time 402 to developboth the most accurate appraisal of current events and actionablerecommendations concerning where the attack may progress and how it maybe mitigated. The information generated in totality is often more thanany one group needs to perform their mitigation tasks. At this point,during a cyberattack, providing a single expansive and all inclusivealert, dashboard image, or report may make identification and actionupon the crucial information by each participant more difficult,therefore the cybersecurity focused arrangement may create multipletargeted information streams each concurrently designed to produce mostrapid and efficacious action throughout the enterprise during the attackand issue follow-up reports with and recommendations or information thatmay lead to long term changes afterward 403. Examples of groups that mayreceive specialized information streams include but may not be limitedto front line responders during the attack 404, incident forensicssupport both during and after the attack 405, chief information securityofficer 406 and chief risk officer 407 the information sent to thelatter two focused to appraise overall damage and to implement bothmitigating strategy and preventive changes after the attack. Front lineresponders may use the cyber-decision platform's analyzed, transformedand correlated information specifically sent to them 404 a to probe theextent of the attack, isolate such things as: the predictive attacker'sentry point onto the enterprise's network, the systems involved or thepredictive ultimate targets of the attack and may use the simulationcapabilities of the system to investigate alternate methods ofsuccessfully ending the attack and repelling the attackers in the mostefficient manner, although many other queries known to those skilled inthe art are also answerable by the invention. Simulations run may alsoinclude the predictive effects of any attack mitigating actions onnormal and critical operation of the enterprise's IT systems andcorporate users. Similarly, a chief information security officer may usethe cyber-decision platform to predictively analyze 406 a what corporateinformation has already been compromised, predictively simulate theultimate information targets of the attack that may or may not have beencompromised and the total impact of the attack what can be done now andin the near future to safeguard that information. Further, duringretrospective forensic inspection of the attack, the forensic respondermay use the cyber-decision platform 405 a to clearly and completely mapthe extent of network infrastructure through predictive simulation andlarge volume data analysis. The forensic analyst may also use theplatform's capabilities to perform a time series and infrastructuralspatial analysis of the attack's progression with methods used toinfiltrate the enterprise's subnets and servers. Again, the chief riskofficer would perform analyses of what information 407 a was stolen andpredictive simulations on what the theft means to the enterprise as timeprogresses. Additionally, the system's predictive capabilities may beemployed to assist in creation of a plan for changes of the ITinfrastructural that should be made that are optimal for remediation ofcybersecurity risk under possibly limited enterprise budgetaryconstraints in place at the company so as to maximize financial outcome.

FIG. 5 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a DCG 500 may comprise a pipeline orchestrator 501 thatmay be used to perform a variety of data transformation functions ondata within a processing pipeline, and may be used with a messagingsystem 510 that enables communication with any number of variousservices and protocols, relaying messages and translating them as neededinto protocol-specific API system calls for interoperability withexternal systems (rather than requiring a particular protocol or serviceto be integrated into a DCG 500).

Pipeline orchestrator 501 may spawn a plurality of child pipelineclusters 502 a-b, which may be used as dedicated workers forstreamlining parallel processing. In some arrangements, an entire dataprocessing pipeline may be passed to a child cluster 502 a for handling,rather than individual processing tasks, enabling each child cluster 502a-b to handle an entire data pipeline in a dedicated fashion to maintainisolated processing of different pipelines using different cluster nodes502 a-b. Pipeline orchestrator 501 may provide a software API forstarting, stopping, submitting, or saving pipelines. When a pipeline isstarted, pipeline orchestrator 501 may send the pipeline information toan available worker node 502 a-b, for example using AKKA™ clustering.For each pipeline initialized by pipeline orchestrator 501, a reportingobject with status information may be maintained. Streaming activitiesmay report the last time an event was processed, and the number ofevents processed. Batch activities may report status messages as theyoccur. Pipeline orchestrator 501 may perform batch caching using, forexample, an IGFS™ caching filesystem. This allows activities 512 a-dwithin a pipeline 502 a-b to pass data contexts to one another, with anynecessary parameter configurations.

A pipeline manager 511 a-b may be spawned for every new runningpipeline, and may be used to send activity, status, lifecycle, and eventcount information to the pipeline orchestrator 501. Within a particularpipeline, a plurality of activity actors 512 a-d may be created by apipeline manager 511 a-b to handle individual tasks, and provide outputto data services 522 a-d. Data models used in a given pipeline may bedetermined by the specific pipeline and activities, as directed by apipeline manager 511 a-b. Each pipeline manager 511 a-b controls anddirects the operation of any activity actors 512 a-d spawned by it. Apipeline process may need to coordinate streaming data between tasks.For this, a pipeline manager 511 a-b may spawn service connectors todynamically create TCP connections between activity instances 512 a-d.Data contexts may be maintained for each individual activity 512 a-d,and may be cached for provision to other activities 512 a-d as needed. Adata context defines how an activity accesses information, and anactivity 512 a-d may process data or simply forward it to a next step.Forwarding data between pipeline steps may route data through astreaming context or batch context.

A client service cluster 530 may operate a plurality of service actors521 a-d to serve the requests of activity actors 512 a-d, ideallymaintaining enough service actors 521 a-d to support each activity perthe service type. These may also be arranged within service clusters 520a-d, in a manner similar to the logical organization of activity actors512 a-d within clusters 502 a-b in a data pipeline. A logging service530 may be used to log and sample DCG requests and messages duringoperation while notification service 540 may be used to receive alertsand other notifications during operation (for example to alert onerrors, which may then be diagnosed by reviewing records from loggingservice 530), and by being connected externally to messaging system 510,logging and notification services can be added, removed, or modifiedduring operation without impacting DCG 500. A plurality of DCG protocols550 a-b may be used to provide structured messaging between a DCG 500and messaging system 510, or to enable messaging system 510 todistribute DCG messages across service clusters 520 a-d as shown. Aservice protocol 560 may be used to define service interactions so thata DCG 500 may be modified without impacting service implementations. Inthis manner it can be appreciated that the overall structure of a systemusing an actor-driven DCG 500 operates in a modular fashion, enablingmodification and substitution of various components without impactingother operations or requiring additional reconfiguration.

FIG. 6 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a variant messaging arrangement may utilize messagingsystem 510 as a messaging broker using a streaming protocol 610,transmitting and receiving messages immediately using messaging system510 as a message broker to bridge communication between service actors521 a-b as needed. Alternately, individual services 522 a-b maycommunicate directly in a batch context 620, using a data contextservice 630 as a broker to batch-process and relay messages betweenservices 522 a-b.

FIG. 7 is a diagram of an exemplary architecture for a system for rapidpredictive analysis of very large data sets using an actor-drivendistributed computational graph 500, according to one aspect. Accordingto the aspect, a variant messaging arrangement may utilize a serviceconnector 710 as a central message broker between a plurality of serviceactors 521 a-b, bridging messages in a streaming context 610 while adata context service 630 continues to provide direct peer-to-peermessaging between individual services 522 a-b in a batch context 620.

It should be appreciated that various combinations and arrangements ofthe system variants described above (referring to FIGS. 1-7) may bepossible, for example using one particular messaging arrangement for onedata pipeline directed by a pipeline manager 511 a-b, while anotherpipeline may utilize a different messaging arrangement (or may notutilize messaging at all). In this manner, a single DCG 500 and pipelineorchestrator 501 may operate individual pipelines in the manner that ismost suited to their particular needs, with dynamic arrangements beingmade possible through design modularity as described above in FIG. 5.

FIG. 19 is block diagram showing an exemplary system architecture 1900for a system for cybersecurity profiling and rating. The system in thisexample contains a cyber-physical graph 1902 which is used to representa complete picture of an organization's infrastructure and operationsincluding, importantly, the organization's computer networkinfrastructure particularly around system configurations that influencecybersecurity protections and resiliency. The system further contains adirected computational graph 1911, which contains representations ofcomplex processing pipelines and is used to control workflows throughthe system such as determining which 3^(rd) party search tools 1915 touse, assigning search tasks, and analyzing the cyber-physical graph 1902and comparing results of the analysis against reconnaissance datareceived from the reconnaissance engine 1906 and stored in thereconnaissance data storage 1905. In some embodiments, the determinationof which 3^(rd) party search tools 1915 to use and assignment of searchtasks may be implemented by a reconnaissance engine 1906. Thecyber-physical graph 1902 plus the analyses of data directed by thedirected computational graph on the reconnaissance data received fromthe reconnaissance engine 1906 are combined to represent thecyber-security profile of the client organization whose network 1907 isbeing evaluated. A queuing system 1912 is used to organize and schedulethe search tasks requested by the reconnaissance engine 1906. A data torule mapper 1904 is used to retrieve laws, policies, and other rulesfrom an authority database 1903 and compare reconnaissance data receivedfrom the reconnaissance engine 1906 and stored in the reconnaissancedata storage 1905 against the rules in order to determine whether and towhat extent the data received indicates a violation of the rules.Machine learning models 1901 may be used to identify patterns and trendsin any aspect of the system, but in this case are being used to identifypatterns and trends in the data which would help the data to rule mapper1904 determine whether and to what extent certain data indicate aviolation of certain rules. A scoring engine 1910 receives the dataanalyses performed by the directed computational graph 1911, the outputof the data to rule mapper 1904, plus event and loss data 1914 andcontextual data 1909 which defines a context in which the other data areto be scored and/or rated. A public-facing proxy network 1908 isestablished outside of a firewall 1917 around the client network 1907both to control access to the client network from the Internet 1913, andto provide the ability to change the outward presentation of the clientnetwork 1907 to the Internet 1913, which may affect the data obtained bythe reconnaissance engine 1906. In some embodiments, certain componentsof the system may operate outside the client network 1907 and may accessthe client network through a secure, encrypted virtual private network(VPN) 1916, as in a cloud-based or platform-as-a-service implementation,but in other embodiments some or all of these components may beinstalled and operated from within the client network 1907.

As a brief overview of operation, information is obtained about theclient network 1907 and the client organization's operations, which isused to construct a cyber-physical graph 1902 representing therelationships between devices, users, resources, and processes in theorganization, and contextualizing cybersecurity information withphysical and logical relationships that represent the flow of data andaccess to data within the organization including, in particular, networksecurity protocols and procedures. The directed computational graph 1911containing workflows and analysis processes, selects one or moreanalyses to be performed on the cyber-physical graph 1902. Some analysesmay be performed on the information contained in the cyber-physicalgraph, and some analyses may be performed on or against thecyber-physical graph using information obtained from the Internet 1913from reconnaissance engine 1906. The workflows contained in the directedcomputational graph 1911 select one or more search tools to obtaininformation about the organization from the Internet 1915, and maycomprise one or more third party search tools 1915 available on theInternet. As data are collected, they are fed into a reconnaissance datastorage 1905, from which they may be retrieved and further analyzed.Comparisons are made between the data obtained from the reconnaissanceengine 1906, the cyber-physical graph 1902, the data to rule mapper,from which comparisons a cybersecurity profile 1918 of the organizationis developed. The cybersecurity profile 1918 is sent to the scoringengine 1910 along with event and loss data 1914 and context data 1909for the scoring engine 1910 to develop a score and/or rating for theorganization that takes into consideration both the cybersecurityprofile 1918, context, and other information.

FIG. 21 is a block diagram showing an exemplary architecture for asoftware analyzer 2100 for a holistic computer system cybersecurityevaluation and scoring system. In this embodiment, the software analyzer2100 comprises a software definition portal 2110, a source code analyzer2120, a compiler 2130, a compiled code analyzer 2140, and one or moredatabase resources 2150. The software definition portal 2110 receiveseither uncompiled, human-readable source code 2111, or compiledmachine-readable binary code 2112 to define the software component ofthe system under test. In this example, definition by specification isnot used, and it is assumed that the software to be tested is provided.If source code 2111 is provided, the software definition portal 2110forwards the source code 2111 to the source code analyzer 2120 forcoding analysis prior to compiling.

The source code analyzer 2120 comprises a coding library analyzer 2121and a coding complexity analyzer 2122. The coding library analyzer 2121searches the code for functions, classes, modules, routines, systemcalls, and other portions of code that rely on or incorporate codecontained in code libraries developed by a different entity than theentity that developed the software under test. Code libraries arecollections of code that have been developed for use in specificcircumstances, such as standardized classes developed for anobject-oriented coding language (e.g., C++, JAVA, etc.), tools developedfor a particular integrated development environment (e.g., Code::Blocks,Eclipse), common libraries for interacting with the operating system,templates, subroutines, etc., that are designed to help speed up,standardize, or make easier the coding of applications. The code in suchlibraries is of varying quality, complexity, usability, security, etc.Code in open source libraries is particularly variable, depending on theskill and knowledge of the (usually part-time, volunteer) contributors,and subject to deprecation if maintenance of the code slows or stops.The source code analyzer 2121 uses this information to determine whichcode libraries are used, what code from the libraries is used, and thesecurity level of that code, and the security level of the source code2111 as a result of using code from those libraries. The coding libraryanalyzer 2121 may access one or more database resources 2150 such asopen source libraries 2151 a, malware databases 2151 b, adversarysimulations (not shown, but e.g., Cobalt Strike), pen testing tools (notshown, but e.g., Meta sploit), post-exploitation agents (not shown, bute.g., Empire), lists of deprecated or out of date software, etc.

The coding complexity analyzer 2122 analyzes the level of additionalcybersecurity risk due to the complexity of the code. As an illustrativeexample, the cyclomatic complexity of a particular software package is astrong indicator of the number of errors that are likely to be in thecode. The cyclomatic complexity of a piece of software is a quantitativemeasure of the number of linearly independent paths through a program'ssource code.

After the source code analyzer 2120 has completed analysis of the sourcecode 2111, the source code 2111 is compiled by a compiler 2130 foroperational testing. The compiler 2130 used will depend on the languagein which the source code 2111 was written. Many different compilers 2130may be available for any given coding language.

Binary code 2112, whether received directly by the software definitionportal 2110 or compiled by the compiler 2130 from source code 2111, issent to a compiled code analyzer 2140 which analyzes the software whileit is in operation (i.e., running) on hardware under an operatingsystem. While the software is running, a function extractor 2141monitors which operations are performed by the software, the order ofsuch operations, and which system resources are accessed by thesoftware, which can disclose the functions, subroutines, etc., that arebeing executed by the compiled code. The characteristics of thosefunctions, subroutines, etc., can be matched to similar functions,subroutines, etc., in coding libraries and such that the functionextractor can identify code from code libraries that are contained in,and being used by, the compiled software. This information about thebinary code 2112 can be sent to the coding library analyzer 2121 foranalysis (typically where such analysis has not already been performedby the source code analyzer 2120). Further, a low-level system accessdetector 2143 will simultaneously monitor the running software toidentify access of, or attempted access of, low-level system resources(e.g., kernel, stack, heap, etc.)

that may indicate cybersecurity concerns. A compiler identifier 2144 canbe used to identify the compiler used to create the binary code 2112 andcertain information about the settings used when during compilation. Inmany cases, compilers embed information in the compiled code such as thecompiler identification, version number, settings, etc., in a commentsection composed of ASCII text. The binary can be scanned for suchtextual information. Alternatively, the binary file can be “decompiled”or “disassembled” in an attempt to match the inputs and outputs of knowncompilers. The compiler identifier 2144 may access one or more databaseresources 2150 to make its determination, such as a database ofcompilers 2151 n and their identifications. An important aspect ofcybersecurity analysis of software is determining whether or not acompiler's safety features were enabled, which is done by the compilersafety feature analyzer 2145. Modern compilers have the ability tosubstitute insecure functions called for in the source code with moresecure versions that perform the same functions. However, if thisfeature is not enabled, the functions will not be substituted.Enablement of the safety features can be determined using the samemethods as for compiler identification. A crash tester 2146 may be usedto determine the robustness of the software to bad inputs or attempts tocrash or hang the software by intentionally inputting improper orunexpected information. Crash logs and reports may be used to gatherdata about particular failures or type of failure, such as the systemlogs created by Windows error reporting, Mac crash reports, and Linuxkdump. The data from these crash logs and reports can be used to performtemporal, graph, and temporal-graph analysis to compare and contrast howlog data, performance and resource data (e.g. statsd type metrics),network connectivity (e.g. systrace collections) and configurationchanges and events, file changes, and software library versions,operating systems, and other factors impact stability, uptime, failurerates and recovery times (e.g. MTTR and MTBF), etc. Further, afunctional efficiency evaluator 2147 may be used to evaluate whether thesoftware does what it purports to do, and its level of efficiency indoing so. For example, if the software is a malware detector, thefunctional efficiency evaluator 2147 may determine whether it functionsas such, and evaluate what percentage of malware introduced into thecomputer system it detects and quarantines.

FIG. 22 is a block diagram showing exemplary elements of a softwaresupply chain 2200 with upstream and downstream sources of cybersecurityvulnerabilities. While not all possible software supply chain elementsare shown here, exemplary categories in the software supply chain areshown, such as the language development level 2210, open source classlibraries 2220, online service providers 2230, the developer of asoftware application of interest 2240, the purchaser of a softwareapplication of interest 2250, sub-contractors 2260, and lower-tiersubcontractors 2270. In this example, risk is being assessed from thelevel of the purchaser 2250 of the software application of interest,shown as the risk assessment level 2282. Cybersecurity risks associatedwith the software application from upstream in the supply chain areshown as upstream risk 2281 relative to the risk assessment level 2282,and cybersecurity risks associated with the software application fromdownstream in the supply chain are shown as downstream risk 2281relative to the risk assessment level 2282.

Language developers 2210 are developers of coding languages 2211 whichare used to code software applications. High-level, object-orientedlanguages such as JAVA, C++, Python, etc., contain code objects calledclasses, which contain pre-designed functionality that can be calledupon to perform certain pre-defined functions. These classes (usuallyorganized into libraries native to the language) can containvulnerabilities that can later be discovered and exploited.

Another major upstream source of vulnerabilities that can beincorporated into a software application are open source code libraries2220, which contain classes designed to add functionality to, or makedevelopment easier for, the particular language for which the class wasdeveloped 2221. Open source libraries 2220 can be written or changed byanyone, and therefore often contain code in classes with vulnerabilitiesthat go unrecognized for some time after a given class is released. Uponcompilation, the classes used from the open source library 2220 areincorporated into the software application, and the vulnerabilities arethus incorporated into the executable binary code.

Online micro-services providers 2230 provide well-maintained, onlinemodules (aka micro-services 2231) that can be linked together to providemore complex functionality. While entire software applications can oftenbe built using micro-services 2231, it is also the case that they can beused to create certain functionality (typically more complexfunctionality) that may not be available in open source code librariesor may be difficult to implement in a stand-alone software application.A software application can be configured to call on functionalitycreated using micro-services 2231, creating a hybrid between amonolithic software application and micro-service 2231 functionality. Asmicro-services 2231 are also software implementations, they can havevulnerabilities, which then expose any software application using themto potential cybersecurity threats.

It is assumed in this example that the software developer is the firstparty in a transaction involving a purchaser, the second party in thetransaction, which one or more lower tier subcontractors 2260 (thirdparty), 2270 (fourth party). As with the coding from other upstreamsources, coding performed by the software developer can introducecybersecurity vulnerabilities into the software application in develops2241. Even where a native class or an open source class contains novulnerabilities, improper implementation of classes can lead tovulnerabilities at the software developer level. For example, the nativestring handler class in a given language may operate properly, but acoding error by the software developer may improperly validate (or failto validate) string inputs, possibly leading to a stack overflow and acrash of the software application, allowing an attacker to access theoperating system on the affected computing device.

In this example the purchaser of the software 2250 is the second partyin the transaction, having purchased the software application (orsoftware-as-a-service) from the software developer 2240. Users of theapplication 2251 are the purchaser level 2250 are subject to the risksassociated with cybersecurity threats, so the risk assessment level 2282in this example is set at the purchaser 2250 level, and all upstreamrisks 2281 and downstream risks 2283 are evaluated from thisperspective. However, the risk assessment level 2282 can be at any levelof the software supply chain. At the purchaser level 2250, the primaryrisk introduced into the supply chain is improper use of the applicationor improper security settings established by the purchaser 2250 or itsIT department.

The downstream risks 2283 associated with subcontractors 2260, 2270 areprimarily related to application dependencies and data provided to thesoftware application 2261, 2271. The software application may depend onfunctionality and/or data provided by subcontractors 2260, which mayfurther depend on functionality and/or data provided by lower tiersubcontractors 2270. While it is not shown in this simplified example,it is also possible that the functionality and/or data provided bysubcontractors 2260, 2270 also uses applications that use open sourceclass libraries 2220 and/or micro-services 2230, in which case thecybersecurity issues for downstream risk 2283 can mirror those forupstream risk 2281.

FIG. 23 is an exemplary cyber-physical graph showing a software supplychain represented as a directed graph with identification of the sourcesof specific software components and possible vulnerabilities. Acyber-physical graph represents the relationships between entitiesassociated with an organization, for example, devices, users, resources,groups, and computing services, the relationships between the entitiesdefining relationships and processes in an organization'sinfrastructure, thereby contextualizing security information withphysical and logical relationships that represent the flow of data andaccess to data within the organization including, in particular, networksecurity protocols and procedures. A cyber-physical graph, in its mostbasic form, is a knowledge graph representing the network devicescomprising an organization's network infrastructure as nodes (alsocalled vertices) in the graph and the physical or logical connectionsbetween them as edges between the nodes. The cyber-physical graph may beexpanded to include network information and processes such as data flow,security protocols and procedures, and software versions and patchinformation. Further, human users and their access privileges to devicesand assets may be included. A cyber-security graph may be furtherexpanded to include internal process information such as businessprocesses, loss information, and legal requirements and documents;external information such as domain and IP information, data breachinformation; and generated information such as open port informationfrom external network scans, and vulnerabilities and avenues of attack.Thus, a cyber-physical graph may be used to represent a complete pictureof an organization's infrastructure and operations.

In the context of this example, the cyber-physical graph represents therelationships between components of a software application, the sourceof those components, and the vulnerabilities associated with thosecomponents. The structure of this cyber-physical graph mirrors therepresentation of the software supply chain shown in a prior drawing.While not all possible software supply chain elements are shown here,exemplary categories in the software supply chain are shown, such as thelanguage development level 2301, open source code libraries 2302, onlineservice providers 2303, the developer of a software application ofinterest 2304, the purchaser of a software application of interest 2305,sub-contractors 2306, and lower-tier subcontractors 2307. Componentsincorporated into, or used by, the software application are representedby nodes in the cyber-physical graph shown here as circles at each levelof the supply chain 2301 a, 2302 a-e, 2303 a-d, 2304 a, 2305 a, 2306a-c, and 2307 a-h, and the relationships between the components areshown as directional edges between the nodes. Vulnerabilities associatedwith each component may be represented by data within the node for thatcomponent or as edge labels (where the vulnerability affects thecomponent or components to which it is attached). The severity of avulnerability or its effects may be designated by an edge weight.

Data may be obtained for the graph through the use of various means,including but not limited to, self-reported data, internetreconnaissance using 3^(rd) party tools as described in FIG. 19, andsoftware analysis as described in FIG. 21. Further, as will be explainedbelow, natural language processing may be used to extract, organize, andutilize information from archives of structured and unstructuredvulnerability data.

In this manner, a comprehensive set of data containing all identified orsuspected vulnerabilities associated with all levels of the softwaresupply chain is created. A comprehensive cybersecurity threat assessmentbased on the totality of vulnerabilities from all levels of the softwaresupply chain may be performed by processing the cyber-physical graph byrunning graph analysis algorithms such as shortest path algorithms,minimum cost/maximum flow algorithms, strongly connected nodealgorithms, etc., to identify the probabilities of success ofcyberattacks through a given vulnerability and the impact of asuccessful cyberattack.

It is important to note that this system not only analyzes static codefeatures, but dynamically updates on a periodic or continuous basis tocapture cybersecurity risks associated with dynamic effects in thesoftware supply chain. Patches, updates, deprecations, changes to EULAsand other licenses, are monitored and updated as they occur, and changesto the software supply chain are propagated through the cyber-physicalgraph. The cyber-physical graph is then re-analyzed to identify new orchanged vulnerability paths in the software supply chain.Vulnerabilities exceeding certain parameters can be established totrigger warnings, alarms, and alerts to notify administrators ofcybersecurity threat/risk levels that exceed the established parameters,and identify precisely which components in the supply chain are causingthe threat/risk, so those components can be addressed (e.g., by removingthat component, service, etc., from the software application oreliminating its use by the software application).

FIG. 24 is a block diagram showing an overall system architecture 2400for a supply chain vulnerability analysis system. The system comprises asoftware analyzer 2401, a cyber-physical graph generator 2402, asoftware component list generator 2403, a scoring engine 2404, andsearch tools optionally including one or more 3^(rd) party search tools2405, and a natural language processing engine 2500. The softwareanalyzer operates in a manner analogous to the software analyzer for theholistic computer system cybersecurity evaluation and scoring systemshown in FIG. 21 and described in the accompanying text. It receives asoftware application in either source code or binary form, analyzes thesoftware application to determine the components that comprise thesoftware application, and the source of those components. The softwareanalyzer 2401 may use tools to search the Internet 2406 to identify thecomponents and the source of those components. The search tools maycomprise one or more 3^(rd) party search tools 2405, some of which maybe cloud-based services accessible through the Internet 2406. Examplesof such search tools and their uses can be found in FIG. 20 anddescribed in the accompanying text. Further, a natural languageprocessing engine 2500 is used to extract, index, and analyze text fromlegal documents associated with the software components such as end userlicense agreements (EULAs), product licenses, terms of use, etc., toidentify changes in the licensing of, and risks associated with,incorporation or use of certain components into a software application.The natural language processing engine may also be used to extract,index, and analyze text from threat information databases which maycontain information associated with the software components such asinformation and files contained in structured threat informationexpression (STIX) databases, trusted automated exchange of intelligenceinformation (TAXII) databases, common vulnerabilities and exposures(CVE) databases, etc. A cyber-physical graph generator 2403 is used tocreate a cyber-physical graph of the components incorporated into orused by the software application, such that analyses may be performed onthe graph to estimate risks associated with various paths in thecyber-physical graph, each of which represents a potential chain ifvulnerability in the software supply chain for that softwareapplication. A software component list generator 2403 creates a list ofcomponents that are incorporated into, or used by, the softwareapplication, and their sources and potential vulnerabilities. Thissoftware component list is analogous to a “bill of materials” forphysical goods, which identifies all components incorporated into aphysical good, and the source of those components. A scoring engine 2404may be used to assign a score to the risks identified in the softwaresupply chain for the software application.

The software component list and scoring can be used to provide averified or certified risk level that can be used in many industrieswhere cybersecurity risk is a concern. One application, in particular,is to certify the level of risk of a software application and its supplychain for purposes of establishing terms and conditions and premiumpricing for cyber liability insurance. While not shown here, the systemmay further comprise a red team/blue team testing module, wherein thesoftware supply chain can be tested by having a red team intentionallyintroduce vulnerabilities into the software supply chain for a givensoftware application to see if the system properly identifies thevulnerability and reports it to the blue team. This concept can beextended further by including a policy database which links industrycompliance requirements (e.g., privacy protection regulations in thefinance or health industries) to certifications of cyber-security ofsoftware applications and their supply chains to demonstrate compliancewith industry compliance regulations. In this manner, the softwarecomponent list and scoring can server as evidentiary documentation ofcompliance and adequacy of controls. Finally, using this system,simulations and parametric evaluations can be run to determine and/orpredict the effect of certain changes, including deterministic and/orstochastic event sets, and a hypothetical cybersecurity score can becreated based on these simulations for each set of conditions.

FIG. 25 is a block diagram showing an exemplary architecture for anatural language processing engine 2500 for the extraction andprocessing of data using natural language processing from labeled,unlabeled, and partially-labeled sources of software vulnerabilityinformation. The natural language processing engine may be used to indexand categorize text from any data source, but in this example is usedprimarily to extract, index, and analyze text from two types ofdocuments, legal documents and threat information databases. Withrespect legal documents, the natural language processing engine is usedto extract, index, and analyze text from legal documents associated withthe software components such as end user license agreements (EULAs),product licenses, terms of use, etc., to identify changes in thelicensing of, and risks associated with, incorporation or use of certaincomponents into a software application. With respect threat informationdatabases, the natural language processing engine is used is used toextract, index, and analyze text from threat information databases whichmay contain information associated with the software components such asinformation and files contained in structured threat informationexpression (STIX) databases, trusted automated exchange of intelligenceinformation (TAXII) databases, common vulnerabilities and exposures(CVE) databases, etc.

In this embodiment, the natural language processing engine comprises oneor more cloud-based storage bins 2501, 2503, 2505, one or more dataextraction steps 2502, 2506, a natural language processor 2504, anon-relational database 2507, and a graphical representation service2508, the operation of which is analogous to the GraphStack service 145,which transforms data into graphical representations for relationalanalysis and may use available third party packages for graph formatdata storage such as Janus and/or a highly interface accessibleprogramming interface such as Akka-Http, although other, similar,combinations may equally serve the same purpose in this role tofacilitate optimal data handling and visualization.

In this embodiment, cloud-based storage bins 2501, 2503, 2505 (e.g.,Amazon S3 storage) are used to store data between processing steps.While cloud-based storage bins are a highly convenient means ofutilizing dynamically-scalable storage, any form of suitable storage maybe used. Data gathered by search tools (e.g., web crawlers, connectorservices, third party data feeds, etc.) is stored in a first cloud-basedstorage bin 2501, from which it is retrieved, and subjected to a firstdata extraction process 2502. The first data extraction process takesdata from structured data sources and processes that data to extractidentifiable information such as software names, vendors names,versions, operating systems, etc., for use in recognizing similarinformation in unstructured data.

Examples of structured data sources are structured threat informationexpression (STIX) databases, trusted automated exchange of intelligenceinformation (TAXII) databases, common vulnerabilities and exposures(CVE) databases, etc. Examples of sources of such structured data arethe National Institute of Standards and Technology (NIST) NationalVulnerability Database (NVD), which provides structured data in XMLformat of known vulnerabilities for a variety of domains, and ReallySimple Syndication (RSS) feeds from companies such as Microsoft'ssecurity advisor RSS feed, which also provides structured data in XMLformat.

The structured data is then sent to storage in a non-relational database2507, while the unstructured data is sent stored in a second cloud-basedstorage bin 2503 for retrieval by a natural language processor 2504. Thenatural language processor 2504 uses the information extracted from thestructured data as training data to search, identify, and tag theunstructured data, thereby converting it to structured data. The taggeddata are then stored in a third cloud-based storage bin 2505 forretrieval and use in a second data extraction process 2506. The seconddata extraction process 2605 processes the tagged data to extractidentifiable information such as software names, vendors names,versions, operating systems, etc., as was the case for the structureddata in the first data extraction process 2502.

The newly structured data (created from unstructured data) are sent tothe database 2507, where they are combined into one or more data storesfor querying. These databases may be a single, large database, or may beseparated into separate databases (e.g. a vulnerability database, asoftware dictionary, an exploit database, etc.). The databases may thenbe processed by a graphical representation service 2508, whichtransforms data into graphical representations for relational analysis.

Detailed Description of Exemplary Aspects

FIG. 8 is a flow diagram of an exemplary method 800 for cybersecuritybehavioral analytics, according to one aspect. According to the aspect,behavior analytics may utilize passive information feeds from aplurality of existing endpoints (for example, including but not limitedto user activity on a network, network performance, or device behavior)to generate security solutions. In an initial step 801, a web crawler115 may passively collect activity information, which may then beprocessed 802 using a DCG 155 to analyze behavior patterns. Based onthis initial analysis, anomalous behavior may be recognized 803 (forexample, based on a threshold of variance from an established pattern ortrend) such as high-risk users or malicious software operators such asbots. These anomalous behaviors may then be used 804 to analyzepotential angles of attack and then produce 805 security suggestionsbased on this second-level analysis and predictions generated by anaction outcome simulation module 125 to determine the likely effects ofthe change. The suggested behaviors may then be automaticallyimplemented 806 as needed. Passive monitoring 801 then continues,collecting information after new security solutions are implemented 806,enabling machine learning to improve operation over time as therelationship between security changes and observed behaviors and threatsare observed and analyzed.

This method 800 for behavioral analytics enables proactive andhigh-speed reactive defense capabilities against a variety ofcyberattack threats, including anomalous human behaviors as well asnonhuman “bad actors” such as automated software bots that may probefor, and then exploit, existing vulnerabilities. Using automatedbehavioral learning in this manner provides a much more responsivesolution than manual intervention, enabling rapid response to threats tomitigate any potential impact. Utilizing machine learning behaviorfurther enhances this approach, providing additional proactive behaviorthat is not possible in simple automated approaches that merely react tothreats as they occur.

FIG. 9 is a flow diagram of an exemplary method 900 for measuring theeffects of cybersecurity attacks, according to one aspect. According tothe aspect, impact assessment of an attack may be measured using a DCG155 to analyze a user account and identify its access capabilities 901(for example, what files, directories, devices or domains an account mayhave access to). This may then be used to generate 902 an impactassessment score for the account, representing the potential risk shouldthat account be compromised. In the event of an incident, the impactassessment score for any compromised accounts may be used to produce a“blast radius” calculation 903, identifying exactly what resources areat risk as a result of the intrusion and where security personnel shouldfocus their attention. To provide proactive security recommendationsthrough a simulation module 125, simulated intrusions may be run 904 toidentify potential blast radius calculations for a variety of attacksand to determine 905 high risk accounts or resources so that securitymay be improved in those key areas rather than focusing on reactivesolutions.

FIG. 10 is a flow diagram of an exemplary method 1000 for continuouscybersecurity monitoring and exploration, according to one aspect.According to the aspect, a state observation service 140 may receivedata from a variety of connected systems 1001 such as (for example,including but not limited to) servers, domains, databases, or userdirectories. This information may be received continuously, passivelycollecting events and monitoring activity over time while feeding 1002collected information into a graphing service 145 for use in producingtime-series graphs 1003 of states and changes over time. This collatedtime-series data may then be used to produce a visualization 1004 ofchanges over time, quantifying collected data into a meaningful andunderstandable format. As new events are recorded, such as changing userroles or permissions, modifying servers or data structures, or otherchanges within a security infrastructure, these events are automaticallyincorporated into the time-series data and visualizations are updatedaccordingly, providing live monitoring of a wealth of information in away that highlights meaningful data without losing detail due to thequantity of data points under examination.

FIG. 11 is a flow diagram of an exemplary method 1100 for mapping acyber-physical system graph (CPG), according to one aspect. According tothe aspect, a cyber-physical system graph may comprise a visualizationof hierarchies and relationships between devices and resources in asecurity infrastructure, contextualizing security information withphysical device relationships that are easily understandable forsecurity personnel and users. In an initial step 1101, behavioranalytics information (as described previously, referring to FIG. 8) maybe received at a graphing service 145 for inclusion in a CPG. In a nextstep 1102, impact assessment scores (as described previously, preferringto FIG. 9) may be received and incorporated in the CPG information,adding risk assessment context to the behavior information. In a nextstep 1103, time-series information (as described previously, referringto FIG. 10) may be received and incorporated, updating CPG informationas changes occur and events are logged. This information may then beused to produce 1104 a graph visualization of users, servers, devices,and other resources correlating physical relationships (such as a user'spersonal computer or smartphone, or physical connections betweenservers) with logical relationships (such as access privileges ordatabase connections), to produce a meaningful and contextualizedvisualization of a security infrastructure that reflects the currentstate of the internal relationships present in the infrastructure.

FIG. 12 is a flow diagram of an exemplary method 1200 for continuousnetwork resilience scoring, according to one aspect. According to theaspect, a baseline score can be used to measure an overall level of riskfor a network infrastructure, and may be compiled by first collecting1201 information on publicly-disclosed vulnerabilities, such as (forexample) using the Internet or common vulnerabilities and exploits (CVE)process. This information may then 1202 be incorporated into a CPG asdescribed previously in FIG. 11, and the combined data of the CPG andthe known vulnerabilities may then be analyzed 1203 to identify therelationships between known vulnerabilities and risks exposed bycomponents of the infrastructure. This produces a combined CPG 1204 thatincorporates both the internal risk level of network resources, useraccounts, and devices as well as the actual risk level based on theanalysis of known vulnerabilities and security risks.

FIG. 13 is a flow diagram of an exemplary method 1300 for cybersecurityprivilege oversight, according to one aspect. According to the aspect,time-series data (as described above, referring to FIG. 10) may becollected 1301 for user accounts, credentials, directories, and otheruser-based privilege and access information. This data may then 1302 beanalyzed to identify changes over time that may affect security, such asmodifying user access privileges or adding new users. The results ofanalysis may be checked 1303 against a CPG (as described previously inFIG. 11), to compare and correlate user directory changes with theactual infrastructure state. This comparison may be used to performaccurate and context-enhanced user directory audits 1304 that identifynot only current user credentials and other user-specific information,but changes to this information over time and how the user informationrelates to the actual infrastructure (for example, credentials thatgrant access to devices and may therefore implicitly grant additionalaccess due to device relationships that were not immediately apparentfrom the user directory alone).

FIG. 14 is a flow diagram of an exemplary method 1400 for cybersecurityrisk management, according to one aspect. According to the aspect,multiple methods described previously may be combined to provide liveassessment of attacks as they occur, by first receiving 1401 time-seriesdata for an infrastructure (as described previously, in FIG. 10) toprovide live monitoring of network events. This data is then enhanced1402 with a CPG (as described above in FIG. 11) to correlate events withactual infrastructure elements, such as servers or accounts. When anevent (for example, an attempted attack against a vulnerable system orresource) occurs 1403, the event is logged in the time-series data 1404,and compared against the CPG 1405 to determine the impact. This isenhanced with the inclusion of impact assessment information 1406 forany affected resources, and the attack is then checked against abaseline score 1407 to determine the full extent of the impact of theattack and any necessary modifications to the infrastructure orpolicies.

FIG. 15 is a flow diagram of an exemplary method 1500 for mitigatingcompromised credential threats, according to one aspect. According tothe aspect, impact assessment scores (as described previously, referringto FIG. 9) may be collected 1501 for user accounts in a directory, sothat the potential impact of any given credential attack is known inadvance of an actual attack event. This information may be combined witha CPG 1502 as described previously in FIG. 11, to contextualize impactassessment scores within the infrastructure (for example, so that it maybe predicted what systems or resources might be at risk for any givencredential attack). A simulated attack may then be performed 1503 to usemachine learning to improve security without waiting for actual attacksto trigger a reactive response. A blast radius assessment (as describedabove in FIG. 9) may be used in response 1504 to determine the effectsof the simulated attack and identify points of weakness, and produce arecommendation report 1505 for improving and hardening theinfrastructure against future attacks.

FIG. 16 is a flow diagram of an exemplary method 1600 for dynamicnetwork and rogue device discovery, according to one aspect. Accordingto the aspect, an advanced cyber decision platform may continuouslymonitor a network in real-time 1601, detecting any changes as theyoccur. When a new connection is detected 1602, a CPG may be updated 1603with the new connection information, which may then be compared againstthe network's resiliency score 1604 to examine for potential risk. Theblast radius metric for any other devices involved in the connection mayalso be checked 1605, to examine the context of the connection for riskpotential (for example, an unknown connection to an internal data serverwith sensitive information may be considered a much higher risk than anunknown connection to an externally-facing web server). If theconnection is a risk, an alert may be sent to an administrator 1606 withthe contextual information for the connection to provide a concisenotification of relevant details for quick handling.

FIG. 17 is a flow diagram of an exemplary method 1700 for Kerberos“golden ticket” attack detection, according to one aspect. Kerberos is anetwork authentication protocol employed across many enterprise networksto enable single sign-on and authentication for enterprise services.This makes it an attractive target for attacks, which can result inpersistent, undetected access to services within a network in what isknown as a “golden ticket” attack. To detect this form of attack,behavioral analytics may be employed to detect erroneously-issuedauthentication tickets, whether from incorrect configuration or from anattack. According to the aspect, an advanced cyber decision platform maycontinuously monitor a network 1701, informing a CPG in real-time of alltraffic associated with people, places, devices, or services 1702.Machine learning algorithms detect behavioral anomalies as they occur inreal-time 1703, notifying administrators with an assessment of theanomalous event 1704 as well as a blast radius score for the particularevent and a network resiliency score to advise of the overall health ofthe network. By automatically detecting unusual behavior and informingan administrator of the anomaly along with contextual information forthe event and network, a compromised ticket is immediately detected whena new authentication connection is made.

FIG. 18 is a flow diagram of an exemplary method 1800 for risk-basedvulnerability and patch management, according to one aspect. Accordingto the aspect, an advanced cyber decision platform may monitor allinformation about a network 1801, including (but not limited to) devicetelemetry data, log files, connections and network events, deployedsoftware versions, or contextual user activity information. Thisinformation is incorporated into a CPG 1802 to maintain an up-to-datemodel of the network in real-time. When a new vulnerability isdiscovered, a blast radius score may be assessed 1803 and the network'sresiliency score may be updated 1804 as needed. A security alert maythen be produced 1805 to notify an administrator of the vulnerabilityand its impact, and a proposed patch may be presented 1806 along withthe predicted effects of the patch on the vulnerability's blast radiusand the overall network resiliency score. This determines both the totalimpact risk of any particular vulnerability, as well as the overalleffect of each vulnerability on the network as a whole. This continuousnetwork assessment may be used to collect information about newvulnerabilities and exploits to provide proactive solutions with clearresult predictions, before attacks occur.

FIG. 20 is a relational diagram showing the relationships betweenexemplary 3^(rd) party search tools 1915, search tasks 2010 that can begenerated using such tools, and the types of information that may begathered with those tasks 2011-2014, and how a public-facing proxynetwork 1908 may be used to influence the search task results. While theuse of 3^(rd) party search tools 1915 is in no way required, andproprietary or other self-developed search tools may be used, there arenumerous 3^(rd) party search tools 1915 available on the Internet, manyof them available for use free of charge, that are convenient forpurposes of performing external and internal reconnaissance of anorganization's infrastructure. Because they are well-known, they areincluded here as examples of the types of search tools that may be usedand the reconnaissance data that may be gathered using such tools. Thesearch tasks 2010 that may be generated may be classified into severalcategories. While this category list is by no means exhaustive, severalimportant categories of reconnaissance data are domain and internetprotocol (IP) address searching tasks 2011, corporate informationsearching tasks 2012, data breach searching tasks 2013, and dark websearching tasks 2014. Third party search tools 1915 for domain and IPaddress searching tasks 2011 include, for example, DNSDumpster,Spiderfoot HX, Shodan, VirusTotal, Dig, Censys, ViewDNS, and CheckDMARC,among others. These tools may be used to obtain reconnaissance dataabout an organization's server IPs, software, geolocation; open ports,patch/setting vulnerabilities; data hosting services, among other data2031. Third party search tools 1915 for corporate information searchingtasks 2012 include, for example, Bloomberg.com, Wikipedia, SEC.gov,AnnualReports.com, DNB.com, Hunter.io, and MarketVisual, among others.These tools may be used to obtain reconnaissance data about anorganization's addresses; corp info; high value target (key employee orkey data assets) lists, emails, phone numbers, online presence 2032.Third party search tools 1915 for data breach searching tasks 2013include, for example, DeHashed, WeLeakInfo, Pastebin, Spiderfoot, andBreachCompilation, among others. These tools may be used to obtainreconnaissance data about an organization's previous data breaches,especially those involving high value targets, and similar data lossinformation 2033. Third party search tools 1915 for deep web (reports,records, and other documents linked to in web pages, but not indexed insearch results . . . estimated to be 90% of available web content) anddark web (websites accessible only through anonymizers such as TOR . . .estimated to be about 6% of available web content) searching tasks 2014include, for example, Pipl, MyLife, Yippy, SurfWax, Wayback machine,Google Scholar, DuckDuckGo, Fazzle, Not Evil, and Start Page, amongothers. These tools may be used to obtain reconnaissance data about anorganization's lost and stolen data such as customer credit cardnumbers, stolen subscription credentials, hacked accounts, softwaretools designed for certain exploits, which organizations are beingtargeted for certain attacks, and similar information 2034. Apublic-facing proxy network 1908 may be used to change the outwardpresentation of the organization's network by conducting the searchesthrough selectable attribution nodes 2021 a-n, which are configurable topresent the network to the Internet in different ways such as, but notlimited to, presenting the organization network as a commercial IPaddress, a residential IP address, or as an IP address from a particularcountry, all of which may influence the reconnaissance data receivedusing certain search tools.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 26, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 26 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVATM compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 27, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 26). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 28, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 27. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 29 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for analyzing the cybersecurity threatof software applications from the software supply chain, comprising: acomputing device comprising a memory and a processor; a softwareanalyzer comprising a first plurality of programming instructions storedin the memory of, and operating on the processor of, the computingdevice, wherein the first plurality of programming instructions, whenoperating on the processor, cause the computing device to: receive asoftware application for analysis; identify one or more softwarecomponents comprising the software application; and send a componentidentifier for each software component identified to a reconnaissanceengine; a reconnaissance engine comprising a second plurality ofprogramming instructions stored in the memory of, and operating on theprocessor of, the computing device, wherein the second plurality ofprogramming instructions, when operating on the processor, cause thecomputing device to: receive the component identifier for the one ormore software components; search one or more databases to identify asource of each software component; search one or more databases toidentify a vulnerability of each software component; send the componentidentifier, source, and vulnerability for each of the one or moresoftware components to a cyber-physical graph engine; a cyber-physicalgraph engine comprising a third plurality of programming instructionsstored in the memory of, and operating on the processor of, thecomputing device, wherein the third plurality of programminginstructions, when operating on the processor, cause the computingdevice to: receive the component identifier, source, and vulnerabilityfor each of the one or more software components; and construct acyber-physical graph of a software supply chain for the softwareapplication, the cyber-physical graph comprising nodes representing thesource and vulnerability of each software component of the softwareapplication and edges representing the relationships between the nodes;and a scoring engine comprising a third plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the third plurality of programminginstructions, when operating on the processor, cause the computingdevice to: run one or more graph-processing algorithms on thecyber-physical graph to determine one or more paths of vulnerability inthe software supply chain and a probability of occurrence for each path;and generate a cybersecurity score for the software application based onthe vulnerabilities in the software supply chain.
 2. The system of claim1, wherein one of the databases used to identify a source of eachsoftware component is a vulnerability information database containingstructured information.
 3. The system of claim 1, wherein one of thedatabases used to identify a vulnerability of each software component isa vulnerability information database containing structured information.4. The system of claim 1, further comprising a natural languageprocessing engine comprising a fourth plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the fourth plurality of programminginstructions, when operating on the processor, cause the computingdevice to: retrieve structured data from a source of vulnerabilityinformation; retrieve unstructured data from a different source ofvulnerability information; extract identifiable information regardingvulnerabilities from the structured data; search, identify, and tag theunstructured data using the identifiable information from the structureddata, thereby converting the unstructured data to newly structured data;and storing a database comprising the newly structured data; wherein oneof the databases used to identify a vulnerability of each softwarecomponent is the database comprising the newly structured data, or oneof the databases used to identify a source of each software component isthe database comprising the newly structured data, or both.
 5. A methodfor analyzing the cybersecurity threat of software applications from thesoftware supply chain, comprising the steps of: receiving a softwareapplication for analysis; identifying one or more software componentscomprising the software application; searching one or more databases toidentify a source of each software component; searching one or moredatabases to identify a vulnerability of each software component;constructing a cyber-physical graph of a software supply chain for thesoftware application, the cyber-physical graph comprising nodesrepresenting the source and vulnerability of each software component ofthe software application and edges representing the relationshipsbetween the nodes; running one or more graph-processing algorithms onthe cyber-physical graph to determine one or more paths of vulnerabilityin the software supply chain and a probability of occurrence for eachpath; and generating a cybersecurity score for the software applicationbased on the vulnerabilities in the software supply chain.
 6. The methodof claim 5, wherein one of the databases used to identify a source ofeach software component is a vulnerability information databasecontaining structured information.
 7. The method of claim 5, wherein oneof the databases used to identify a vulnerability of each softwarecomponent is a vulnerability information database containing structuredinformation.
 8. The method of claim 5, further comprising the steps of:retrieving structured data from a source of vulnerability information;retrieving unstructured data from a different source of vulnerabilityinformation; extracting identifiable information regardingvulnerabilities from the structured data; searching, identifying, andtagging the unstructured data using the identifiable information fromthe structured data, thereby converting the unstructured data to newlystructured data; storing a database comprising the newly structureddata; and using the database comprising the newly structured data toidentify a vulnerability of each software component, or to identify asource of each software component, or both.